Metamodel Refactoring using Constraint Solving: a Quality-based Perspective
Bibliographic record
Abstract
The design of metamodels is a main task in model-driven engineering where modellers need to consider many quality factors. However, metamodels are subject to many changes during the software life cycle due to the evolution of requirements or for maintenance purposes. These changes may harm their quality by introducing bad smells that make the metamodels more complex and less understandable. Refactoring metamodels by removing bad smells is not an easy task due to their size, the need to achieve high standards of conflicting quality factors, and the many possible refactoring solutions. We propose a quality-driven approach to refactoring metamodels using constraint solving. We encode both the removal of bad smells and the quality criteria as a set of constraints. Then, we use a constraint solver to find a sequence of refactoring operations that satisfies both constraints. We illustrate the efficiency of our approach through a case study. The latter shows that the refactoring solution we obtain improves the time and correctness of performing understandability and extendibility tasks, as compared to other alternatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".